2y ago

AIs can guess where Reddit users live and how much they earn

www.newscientist.com

Large language models (LLMs) like GPT-4 can identify a person’s age, location, gender and income with up to 85 per cent accuracy simply by analysing their posts on social media.

But the AIs also picked up on subtler cues, like location-specific slang, and could estimate a salary range from a user’s profession and location.

Reference:

arXiv DOI: 10.48550/arXiv.2310.07298

23 comments

You could guess California, software dev, 55K/year and be right like half the time.
- That seems low for a California software dev. Maybe in the Midwest?
Okay, I think I must absolutely be misreading this. They started with 1500 potential accounts, then picked 500 that, by hand, they could make guesses about based on people doing things like actually posting where they live or how much they make.
And then they’re claiming their LLMs have 85% accuracy based on that subset of data? There has to be more than this. Were they 85% on the full 1500? How did they confirm that? Was it just on the 500? Then what’s the point?
There was a study on Facebook that showed that they could predict with between 80-95% accuracy (or some crazy number like that) your gender, orientation, politics, and so on just based on your public likes. That was ten years ago at least. What is this even showing?
- SnoopSnoo was able to pick out phrases from Reddit posters based on declarative statements they made in their posts, and that site has been down for years.
Well only because I've said where I live and how much I earn, before.
You can also do that without AI. We've had metadata analysis for a while now.
- Sure, but AI is the hot buzzword right now, so it's got to be shoehorned into every discussion about technology!
  
  I think it's overall a good thing if it helps laymen understand just how much privacy matters and how much can be gleaned from seemingly innocuous data online. If an "AI" label makes it hit home, cool. As long as they get it.
- As is typical, this science reporting isn't great. It's not only that AI can do it effectively, but that it can do it at scale. To quote the paper:
  "Despite these models achieving near-expert human performance, they come at a fraction of the cost, requiring 100× less financial and 240× lower time investment than human labelers—making such privacy violations at scale possible for the first time."
  They also demonstrate how interacting with an AI model can quickly extract more private info without looking like it is. A game of 20 questions, except you don't realize you're playing.
- That's what facebook/google have been doing for years without AI.
- Yup, and plenty of people have no issues posting about local events or joining region/city specific groups, so it's not exactly hard to put two and two together.
  I don't have much issue posting about the city I grew up in or former jobs, but generally work at being fairly vague about anything current
I wonder how long it will take for the media to get past the "AI is GOD DAMN AMAZING" phase and start real journalism about AI.
Seriously, neural networks have existed since the 1990s. The tech is not all that amazing, really.
Find someone that can explain what's going on inside a neural net. Then I'll be impressed.
- Explaining what happens in a neural net is trivial. All they do is approximate (generally) nonlinear functions with a long series of multiplications and some rectification operations.
  That isn't the hard part, you can track all of the math at each step.
  The hard part is stating a simple explanation for the semantic meaning of each operation.
  When a human solves a problem, we like to think that it occurs in discrete steps with simple goals: "First I will draw a diagram and put in the known information, then I will write the governing equations, then simplify them for the physics of the problem", and so on.
  Neural nets don't appear to solve problems that way, each atomic operation does not have that semantic meaning. That is the root of all the reporting about how they are such 'black boxes' and researchers 'don't understand' how they work.
  
  When a human solves a problem, we like to think that it occurs in discrete steps with simple goals: "First I will draw a diagram and put in the known information, then I will write the governing equations, then simplify them for the physics of the problem", and so on.
  I wonder how our brain even comes to formulate these steps in a way we can comprehend, the amount of neurons and zones firing on all cylinders seems tiring to imagine
  
  Yeah but most people don't know this and have never looked. It seems way more complex to the layman than it is because instinctually we assume that anything that accomplishes great feats must be incredibly intricate
My city's subreddit did a thread a while back asking people what they were making in the area for what jobs, to try to crowd source salary transparency. So this is not very impressive lol
They can probably do that with any social media including lemmy...
Well, if you look at the subreddits where a Redditor posts and there's a lot of r/Seattle or Washington State then it's not that hard to deduce.
Although I try to leave a mild aura of mystery around my personal life, it wouldn't be hard to snoop around a bit to find details here and there about me.
- At least your scat fetish is kept on the down low.
I tried asking Bing to make an assumption about who I am based on my Reddit account and wrote a nonsense made up story. Maybe I could have phrased it better?
- This is basically what an LLM does, making up stories that might seem correct.
  
  Confidently making up stories.

23 comments